General Reasoning B Verified

RL environment company · Finance

Domains: Finance Coding Long-Horizon
On X: @GenReasoning ↗
Tier: B why
Snapshot: November 5, 2026

What General Reasoning does

General Reasoning is an AI research lab that runs OpenReward, a platform for serving and running RL environments, and also sells long-horizon benchmarks and BackSearch, a point-in-time web search API for evals and training.

Sells: RL environments and Data
Leads with: Coding
Based in: London, UK
Team: 1-10
Website: gr.inc ↗

What General Reasoning sells

Read from General Reasoning's own website, checked October 11, 2026: the line that puts it in each category.

RL environments

RL environments
“An open resource for discovering and experimenting with community RL environments with simple API endpoints and autoscaled infrastructure.”
gr.inc/ ↗
Evals
“A benchmark for long-horizon sequential decision making.”
gr.inc/ ↗

Data expanding into

Datasets expanding into
“A frozen archive of news, SEC filings, arXiv, Wikipedia and the wider web, with point-in-time search and fetch for forecasting and reproducible evals and training.”
gr.inc/ ↗

Domains General Reasoning covers

Coding leads with
“a benchmark of 89 challenging terminal-based software engineering tasks spanning compilation, cryptography, data processing, machine learning, system administration and scientific computing”
gr.inc/releases/introducing-firehorse ↗
Finance
“Every frontier AI model lost money when tasked with betting on Premier League matches over a full season, a new benchmark reveals.”
gr.inc/ ↗

General Reasoning competitors and alternatives

The companies that sell the most of the same things in the same domains, and what they share with General Reasoning.

Revelo Also sells RL environments and evals · also in coding and finance
Abundant Also sells RL environments and evals · also in coding
Bespoke Labs Also sells RL environments and evals · also in coding
Datacurve Also sells RL environments and evals · also in coding
Emulated Also sells RL environments and evals · also in coding

Every RL environment company on the landscape map →

Tier B

Growing: 11 to 50 people, or $3M+ raised.

How much stands behind a tier: usage, who backs it, a confirmed line from its own site or paper, and a clear read on X. High with three of the four, medium with two, low otherwise.

team 11-50
raised $11M
investors: Entrepreneur First
in curated RL directories (5 pts)

On X · unclear, low buzz, from 7 posts about General Reasoning in the last month

Little discussion; a Japanese post summarizes its RLCR/Avalon-M1 multi-agent organization results, plus a few replies, with no customer or lab commentary on OpenReward or BackSearch.

@M0EGPT ↗ · @SasuRobert ↗ · @strickvl ↗

How we got to tier B

Adoption: team 11-50; raised $11M; investors: Entrepreneur First. Tier B.
On X: unclear, low buzz. “Little discussion; a Japanese post summarizes its RLCR/Avalon-M1 multi-agent organization results, plus a few replies, with no customer or lab commentary on OpenReward or BackSearch.” No move.
In curated RL directories: 5 points, a floor of C.
Confirmed: a line from its own site or paper places it.

A clear view on X moves a tier one step up or down; most entries don't move. How tiers work.

Where General Reasoning sits

Its domain on the map, live. Hover a logo for its name; click to open it. Open it on the full map

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Latest from General Reasoning on X

@GenReasoning ↗

🐝 How to Train a Swarm In our first blog post, we explain how latency pressure can incentivise parallel work, the communication primitives agents need to co-ordinate, and outstanding challenges in training large agent collectives.
— GenReasoning (@GenReasoning) Nov 2, 2026
Hear the story of how we developed Avalon at the London AO Summit hosted by @ARIA_research. We’ll be talking not only about the results, but the research process of how we got there, and what our future roadmap looks like.
— GenReasoning (@GenReasoning) Oct 28, 2026
Introducing Reinforcement Learning from Collective Rewards (RLCR). RLCR is a recipe that trains language models as agent organisations, which share a message board and workspace. We use it to train Avalon-M1-Mini (120B) and study its behaviour. We find a number of interesting results, including: 🛠️ Learnt organisational hierarchies and divisions of labour 🕵️ Increased collective verification, through hires of verifiers and testers 👥 The rise of collective thinking, where agents think more about other agents and how to help them We believe RLCR can provide a laboratory for studying collective intelligence and multi-agent safety. Blog post and technical report 👇
— GenReasoning (@GenReasoning) Oct 23, 2026

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